In many missions, the AOR is confronted with scenes having high-density clutter. This clutter is caused by many objects that look like targets. This ultimately causes high false alarm rates. Reducing these rates requires tuning a number of AOR parameters.
A critical problem in AOR systems is multi-scenario adaptation. Present AOR systems perform well in certain scenarios, but they perform poorly and unpredictably in others. Unless AOR systems can be adapted, their utility in battlefield missions remains questionable.
Object contrast may vary quickly due to dust, atmospheric and sensor perturbations, image formation and phenomenological reasons. Such variation can cause the AOR to miss or breakup the object. Adjusting the bright thresholds, among other parameters, can compensate for contrast variations.
Multi-scenario adaptability is the most serious and challenging problem in AOR technology. AOR adaptation is not just a desirable feature but rather a very critical functional requirement.
The problem of multi-scenario adaptation was formerly realized in the AOR technology. It was understood art that algorithms would perform well with whatever assumptions they were based upon, and that they would detect and recognize objects similar to the ones they were trained on. However, it was also assumed that the algorithms were flexible enough to span a wide range of scenarios. This assumption proved to be wrong when AORs were tested in the field. Real world scenarios and terrain boards showed that there was too much variation in the content, context and quality of the images, and that the AORs were designed to deal with only a small subset of them.
One promising aspect in solving the adaptation problem was that most AOR systems are parameterized. Tests showed that tuning a number of algorithm parameters improved AOR performance. In many tests, the improvement was dramatic. Manually-performed parameter adaptation was used for tuning the parameters. Such parameter adaptation required a human expert because it required intimate knowledge of the AOR algorithms and its many parameters.
As a result, the AOR adaptation challenge was focused on automatic adaptation of parameters. Adaptation came to mean tuning AOR parameters based on observation of the scene and not on AOR internal processing results, such as adaptive thresholding and background adaptation. A number of approaches were conceived to deal with this problem, but none appeared satisfactory to the present applicants.